介紹
在進行數值計算、音頻處理或者圖像分析時,性能往往是影響用戶體驗的關鍵因素。為了讓開發者能更方便地利用底層硬件的高性能向量計算能力,Apple 提供了Accelerate
框架。它封裝了底層的 SIMD 指令集與高效的數學計算函數,可以用極少的代碼實現高效、并行的計算操作,同時還能節省電量。Accelerate
支持在 iOS、macOS、tvOS 與 watchOS 上運行。常用子模塊如下。
- vDSP:用于數字信號處理。
- vForce:用于向量化的數學函數。
- BNNS/BNNSGraph:基礎神經網絡子系統。
- LAPACK/BLAS:用于線性代數計算。
vDSP使用
- 求平均數。
import Accelerate
let values: [Float] = [1, 2, 3, 4, 5, 6, 7, 8, 9]
let mean = vDSP.mean(values)
print(mean)
- 求標準差。
import Accelerate
let values: [Float] = [1, 2, 3, 4, 5, 6, 7, 8, 9]
let sd = vDSP.standardDeviation(values)
print(sd)
- 向量加法。
import Accelerate
let a: [Float] = [1, 2, 3]
let b: [Float] = [4, 5, 6]
let result = vDSP.add(a, b)
print(result)
vForce使用
- 計算正弦函數。
import Accelerate
let angles: [Float] = [0, .pi / 4, .pi / 2, .pi]
let sins = vForce.sin(angles)
print(sins)
- 計算對數。
import Accelerate
let inputs: [Float] = [1, 2.71828, 10]
let logs = vForce.log(inputs)
print(logs)
- 計算指數。
import Accelerate
let values: [Float] = [0, 1, 2]
let exps = vForce.exp(values)
print(exps)
BNNS/BNNSGraph使用
- 構造神經網絡。
import Accelerate
let input: [Float] = [1.0, 2.0, 3.0]
var weights: [Float] = [0.1, 0.2, 0.3,
0.4, 0.5, 0.6]
var bias: [Float] = [0.1, 0.2]
var output = [Float](repeating: 0, count: 2)
var inDesc = BNNSVectorDescriptor(size: 3, data_type: .float, data_scale: 1.0, data_bias: 0.0)
var outDesc = BNNSVectorDescriptor(size: 2, data_type: .float, data_scale: 1.0, data_bias: 0.0)
let weightData = BNNSLayerData(data: &weights, data_type: .float)
let biasData = BNNSLayerData(data: &bias, data_type: .float)
var layer = BNNSFullyConnectedLayerParameters(
in_size: 3,
out_size: 2,
weights: weightData,
bias: biasData,
activation: BNNSActivation(function: .identity, alpha: 0, beta: 0)
)
var filterParams = BNNSFilterParameters()
let layerFilter = BNNSFilterCreateFullyConnectedLayer(&inDesc, &outDesc, &layer, &filterParams)
BNNSFilterApply(layerFilter!, input, &output)
print("BNNS Output:", output)
BNNSFilterDestroy(layerFilter)
LAPACK/BLAS使用
- 矩陣乘法。
import Accelerate
let A: [Float] = [1, 2,
3, 4]
let B: [Float] = [5, 6,
7, 8]
var C: [Float] = [0, 0,
0, 0]
let m = 2, n = 2, k = 2
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans,
Int32(m), Int32(n), Int32(k), 1.0,
A, Int32(k), B, Int32(n), 0.0, &C, Int32(n))
print(C)
應用:圖像高斯模糊處理
- 代碼。
import Accelerate
import UIKit
class ViewController: UIViewController {
lazy var imageView: UIImageView = {
let imageView = UIImageView(frame: CGRect(x: 0, y: 0, width: 300, height: 300))
imageView.image = UIImage(named: "sample")
imageView.center = view.center
return imageView
}()
override func viewDidLoad() {
super.viewDidLoad()
view.addSubview(imageView)
}
override func touchesBegan(_ touches: Set<UITouch>, with event: UIEvent?) {
blurImage()
}
func blurImage() {
if let image = UIImage(named: "sample") {
if let blurredImage = ImageProcessor.applyGaussianBlur(to: image, withRadius: 30) {
imageView.image = blurredImage
} else {
print("無法應用高斯模糊效果")
}
}
}
}
class ImageProcessor {
/// 使用vImage對圖像進行高斯模糊處理
static func applyGaussianBlur(to image: UIImage, withRadius radius: Float) -> UIImage? {
guard let cgImage = image.cgImage else { return nil }
// 創建圖像格式
var format = vImage_CGImageFormat(
bitsPerComponent: 8,
bitsPerPixel: 32,
colorSpace: Unmanaged.passRetained(CGColorSpaceCreateDeviceRGB()),
bitmapInfo: CGBitmapInfo(rawValue: CGImageAlphaInfo.premultipliedFirst.rawValue | CGBitmapInfo.byteOrder32Little.rawValue),
version: 0,
decode: nil,
renderingIntent: .defaultIntent
)
// 創建vImage緩沖區
var sourceBuffer = vImage_Buffer()
var error = vImageBuffer_InitWithCGImage(&sourceBuffer, &format, nil, cgImage, vImage_Flags(kvImageNoFlags))
guard error == kvImageNoError else { return nil }
// 創建目標緩沖區
var destinationBuffer = vImage_Buffer()
destinationBuffer.data = malloc(sourceBuffer.rowBytes * Int(sourceBuffer.height))
destinationBuffer.width = sourceBuffer.width
destinationBuffer.height = sourceBuffer.height
destinationBuffer.rowBytes = sourceBuffer.rowBytes
guard destinationBuffer.data != nil else {
free(sourceBuffer.data)
return nil
}
// 卷積核大小
let kernelSize = UInt32(max(3, Int(radius) | 1))
// 應用高斯模糊
error = vImageTentConvolve_ARGB8888(&sourceBuffer,
&destinationBuffer,
nil,
0, 0,
kernelSize, kernelSize,
nil,
vImage_Flags(kvImageEdgeExtend))
guard error == kvImageNoError else {
free(sourceBuffer.data)
free(destinationBuffer.data)
return nil
}
// 從vImage緩沖區創建新的CGImage
guard let blurredImage = vImageCreateCGImageFromBuffer(&destinationBuffer,
&format,
nil,
nil,
vImage_Flags(kvImageNoFlags),
&error) else {
free(sourceBuffer.data)
free(destinationBuffer.data)
return nil
}
// 釋放內存
free(sourceBuffer.data)
free(destinationBuffer.data)
if error != kvImageNoError {
return nil
}
// 從CGImage創建UIImage
return UIImage(cgImage: blurredImage.takeRetainedValue())
}
}
- 效果。
效果.gif
總結
Accelerate
框架雖然接口相對底層,但非常適合需要處理大量浮點數據的場景,它提供了系統級別的性能優化支持,是開發高性能 App 的得力工具。